
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowercase    :   List[Any]						=			logging.get_logger(__name__)

lowercase    :   List[str]						=			'▁'

lowercase    :   str						=			{'vocab_file': 'sentencepiece.bpe.model'}

lowercase    :   int						=			{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

lowercase    :   Optional[int]						=			{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
lowercase    :   Tuple						=			['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	VOCAB_FILES_NAMES
	_A								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=	PRETRAINED_VOCAB_FILES_MAP
	_A								=	['input_ids', 'attention_mask']

	_A								=	[]
	_A								=	[]







	def __init__(   self						:Any	,  a						:Union[str, Any]	,  a						:List[str]=None	,  a						:Tuple=None	,  a						:Optional[int]="</s>"	,  a						:Tuple="</s>"	,  a						:Optional[int]="<s>"	,  a						:Any="<unk>"	,  a						:Dict="<pad>"	,  a						:int="<mask>"	,  a						:Optional[Dict[str, Any]] = None	,  **a						:Tuple	,  )						->					None:
								# Mask token behave like a normal word, i.e. include the space before it
								__UpperCamelCase      :  Dict       =   AddedToken(a	,  lstrip=a	,  rstrip=a		) if isinstance(a	,  a		) else mask_token

								__UpperCamelCase      :  Dict       =   {} if sp_model_kwargs is None else sp_model_kwargs

								__UpperCamelCase      :  Any       =   kwargs.get("additional_special_tokens"	,  []		)
								kwargs["additional_special_tokens"] += [
								    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    src_lang=a	,  tgt_lang=a	,  eos_token=a	,  unk_token=a	,  sep_token=a	,  cls_token=a	,  pad_token=a	,  mask_token=a	,  sp_model_kwargs=self.sp_model_kwargs	,  **a	,  )

								__UpperCamelCase      :  List[str]       =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(str(a		)		)
								__UpperCamelCase      :  Union[str, Any]       =   vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# Mimic fairseq token-to-id alignment for the first 4 token
								__UpperCamelCase      :  List[Any]       =   {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								__UpperCamelCase      :  Union[str, Any]       =   1

								__UpperCamelCase      :  Any       =   len(self.sp_model		)
								__UpperCamelCase      :  Dict       =   {
								    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a		)
								}
								__UpperCamelCase      :  Union[str, Any]       =   {v: k for k, v in self.lang_code_to_id.items()}
								__UpperCamelCase      :  Tuple       =   len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

								self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
								__UpperCamelCase      :  int       =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}

								__UpperCamelCase      :  List[Any]       =   src_lang if src_lang is not None else "en_XX"
								__UpperCamelCase      :  str       =   self.lang_code_to_id[self._src_lang]
								__UpperCamelCase      :  Optional[Any]       =   tgt_lang
								self.set_src_lang_special_tokens(self._src_lang		)







	@property
	def   _lowerCamelCase							(   self						:Dict		)						->					int:
								return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token







	@property
	def   _lowerCamelCase							(   self						:Tuple		)						->					str:
								return self._src_lang







	@src_lang.setter
	def   _lowerCamelCase							(   self						:Any	,  a						:str		)						->					None:
								__UpperCamelCase      :  Any       =   new_src_lang
								self.set_src_lang_special_tokens(self._src_lang		)







	def __getstate__(   self						:Optional[int]		)						->					Dict:
								__UpperCamelCase      :  Optional[int]       =   self.__dict__.copy()
								__UpperCamelCase      :  List[str]       =   None
								return state







	def __setstate__(   self						:Dict	,  a						:Dict		)						->					None:
								__UpperCamelCase      :  Tuple       =   d

								# for backward compatibility
								if not hasattr(self	,  "sp_model_kwargs"		):
															__UpperCamelCase      :  Dict       =   {}

								__UpperCamelCase      :  Union[str, Any]       =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	def   _lowerCamelCase							(   self						:Dict		)						->					Dict:
								__UpperCamelCase      :  str       =   {self.convert_ids_to_tokens(a		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:str		)						->					List[str]:
								return self.sp_model.encode(a	,  out_type=a		)







	def   _lowerCamelCase							(   self						:Dict	,  a						:str		)						->					int:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								__UpperCamelCase      :  Any       =   self.sp_model.PieceToId(a		)

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







	def   _lowerCamelCase							(   self						:str	,  a						:int		)						->					str:
								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:Any		)						->					Tuple:
								__UpperCamelCase      :  Tuple       =   []
								__UpperCamelCase      :  int       =   ""
								__UpperCamelCase      :  Tuple       =   False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(a		) + token
																						__UpperCamelCase      :  str       =   True
																						__UpperCamelCase      :  Any       =   []
															else:
																						current_sub_tokens.append(a		)
																						__UpperCamelCase      :  List[str]       =   False
								out_string += self.sp_model.decode(a		)
								return out_string.strip()







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:str	,  a						:Optional[str] = None		)						->					Tuple[str]:
								if not os.path.isdir(a		):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
															return
								__UpperCamelCase      :  Dict       =   os.path.join(
								    a	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(a		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file	,  a		)
								elif not os.path.isfile(self.vocab_file		):
															with open(a	,  "wb"		) as fi:
																						__UpperCamelCase      :  List[Any]       =   self.sp_model.serialized_model_proto()
																						fi.write(a		)

								return (out_vocab_file,)







	def   _lowerCamelCase							(   self						:Any	,  a						:List[int]	,  a						:Optional[List[int]] = None	,  a						:bool = False		)						->					List[int]:

								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=a	,  token_ids_a=a	,  already_has_special_tokens=a		)

								__UpperCamelCase      :  Optional[Any]       =   [1] * len(self.prefix_tokens		)
								__UpperCamelCase      :  Optional[int]       =   [1] * len(self.suffix_tokens		)
								if token_ids_a is None:
															return prefix_ones + ([0] * len(a		)) + suffix_ones
								return prefix_ones + ([0] * len(a		)) + ([0] * len(a		)) + suffix_ones







	def   _lowerCamelCase							(   self						:int	,  a						:List[int]	,  a						:Optional[List[int]] = None		)						->					List[int]:
								if token_ids_a is None:
															return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







	def   _lowerCamelCase							(   self						:Optional[int]	,  a						:Any	,  a						:str	,  a						:Optional[str]	,  a						:Optional[str]	,  **a						:Tuple		)						->					str:
								if src_lang is None or tgt_lang is None:
															raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"		)
								__UpperCamelCase      :  str       =   src_lang
								__UpperCamelCase      :  Dict       =   self(a	,  add_special_tokens=a	,  return_tensors=a	,  **a		)
								__UpperCamelCase      :  Union[str, Any]       =   self.convert_tokens_to_ids(a		)
								__UpperCamelCase      :  Tuple       =   tgt_lang_id
								return inputs







	def   _lowerCamelCase							(   self						:int	,  a						:List[str]	,  a						:str = "en_XX"	,  a						:Optional[List[str]] = None	,  a						:str = "ro_RO"	,  **a						:Dict	,  )						->					BatchEncoding:
								__UpperCamelCase      :  Optional[Any]       =   src_lang
								__UpperCamelCase      :  int       =   tgt_lang
								return super().prepare_seqaseq_batch(a	,  a	,  **a		)







	def   _lowerCamelCase							(   self						:Dict		)						->					List[str]:
								return self.set_src_lang_special_tokens(self.src_lang		)







	def   _lowerCamelCase							(   self						:str		)						->					Any:
								return self.set_tgt_lang_special_tokens(self.tgt_lang		)







	def   _lowerCamelCase							(   self						:Dict	,  a						:str		)						->					None:
								__UpperCamelCase      :  Optional[Any]       =   self.lang_code_to_id[src_lang]
								__UpperCamelCase      :  List[Any]       =   [self.cur_lang_code_id]
								__UpperCamelCase      :  Optional[Any]       =   [self.eos_token_id]







	def   _lowerCamelCase							(   self						:str	,  a						:str		)						->					None:
								__UpperCamelCase      :  List[Any]       =   self.lang_code_to_id[tgt_lang]
								__UpperCamelCase      :  Any       =   [self.cur_lang_code_id]
								__UpperCamelCase      :  Any       =   [self.eos_token_id]

import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase    :   List[str]						=			get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	ReformerTokenizer
	_A								=	ReformerTokenizerFast
	_A								=	True
	_A								=	False
	_A								=	True







	def   _lowerCamelCase							(   self						:Dict		)						->					Union[str, Any]:
								super().setUp()

								__UpperCamelCase      :  List[str]       =   ReformerTokenizer(a	,  keep_accents=a		)
								tokenizer.save_pretrained(self.tmpdirname		)







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   "<s>"
								__UpperCamelCase      :  Dict       =   1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(a		)	,  a		)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(a		)	,  a		)







	def   _lowerCamelCase							(   self						:Dict		)						->					List[Any]:
								__UpperCamelCase      :  int       =   list(self.get_tokenizer().get_vocab().keys()		)

								self.assertEqual(vocab_keys[0]	,  "<unk>"		)
								self.assertEqual(vocab_keys[1]	,  "<s>"		)
								self.assertEqual(vocab_keys[-1]	,  "j"		)
								self.assertEqual(len(a		)	,  1_0_0_0		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					str:
								self.assertEqual(self.get_tokenizer().vocab_size	,  1_0_0_0		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					List[str]:
								if not self.test_rust_tokenizer:
															return

								__UpperCamelCase      :  Union[str, Any]       =   self.get_tokenizer()
								__UpperCamelCase      :  List[str]       =   self.get_rust_tokenizer()

								__UpperCamelCase      :  Union[str, Any]       =   "I was born in 92000, and this is falsé."

								__UpperCamelCase      :  List[str]       =   tokenizer.tokenize(a		)
								__UpperCamelCase      :  int       =   rust_tokenizer.tokenize(a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  List[str]       =   tokenizer.encode(a	,  add_special_tokens=a		)
								__UpperCamelCase      :  Tuple       =   rust_tokenizer.encode(a	,  add_special_tokens=a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  Union[str, Any]       =   self.get_rust_tokenizer()
								__UpperCamelCase      :  int       =   tokenizer.encode(a		)
								__UpperCamelCase      :  List[Any]       =   rust_tokenizer.encode(a		)
								self.assertListEqual(a	,  a		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:int=1_5		)						->					Any:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
																						__UpperCamelCase      :  List[Any]       =   self.rust_tokenizer_class.from_pretrained(a	,  **a		)

																						# Simple input
																						__UpperCamelCase      :  Union[str, Any]       =   "This is a simple input"
																						__UpperCamelCase      :  Dict       =   ["This is a simple input 1", "This is a simple input 2"]
																						__UpperCamelCase      :  Tuple       =   ("This is a simple input", "This is a pair")
																						__UpperCamelCase      :  Optional[Any]       =   [
																						    ("This is a simple input 1", "This is a simple input 2"),
																						    ("This is a simple pair 1", "This is a simple pair 2"),
																						]

																						# Simple input tests
																						self.assertRaises(a	,  tokenizer_r.encode	,  a	,  max_length=a	,  padding="max_length"		)

																						# Simple input
																						self.assertRaises(a	,  tokenizer_r.encode_plus	,  a	,  max_length=a	,  padding="max_length"		)

																						# Simple input
																						self.assertRaises(
																						    a	,  tokenizer_r.batch_encode_plus	,  a	,  max_length=a	,  padding="max_length"	,  )

																						# Pair input
																						self.assertRaises(a	,  tokenizer_r.encode	,  a	,  max_length=a	,  padding="max_length"		)

																						# Pair input
																						self.assertRaises(a	,  tokenizer_r.encode_plus	,  a	,  max_length=a	,  padding="max_length"		)

																						# Pair input
																						self.assertRaises(
																						    a	,  tokenizer_r.batch_encode_plus	,  a	,  max_length=a	,  padding="max_length"	,  )







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					str:
								pass







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Optional[Any]:
								__UpperCamelCase      :  List[Any]       =   ReformerTokenizer(a	,  keep_accents=a		)

								__UpperCamelCase      :  Tuple       =   tokenizer.tokenize("This is a test"		)
								self.assertListEqual(a	,  ["▁This", "▁is", "▁a", "▁t", "est"]		)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(a		)	,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]	,  )

								__UpperCamelCase      :  str       =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
								self.assertListEqual(
								    a	,  [
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "9",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "é",
								        ".",
								    ]	,  )
								__UpperCamelCase      :  Union[str, Any]       =   tokenizer.convert_tokens_to_ids(a		)
								self.assertListEqual(
								    a	,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]	,  )

								__UpperCamelCase      :  Union[str, Any]       =   tokenizer.convert_ids_to_tokens(a		)
								self.assertListEqual(
								    a	,  [
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "<unk>",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "<unk>",
								        ".",
								    ]	,  )







	@cached_property
	def   _lowerCamelCase							(   self						:Any		)						->					List[str]:
								return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   "Hello World!"
								__UpperCamelCase      :  Union[str, Any]       =   [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

								self.assertListEqual(a	,  self.big_tokenizer.encode(a		)		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					List[str]:
								__UpperCamelCase      :  Dict       =   (
								    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
								    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
								)
								__UpperCamelCase      :  List[Any]       =   [
								    1_0_8,
								    2_6_5,
								    2_4,
								    1_1_1,
								    4,
								    2_5_8,
								    1_5_6,
								    3_5,
								    2_8,
								    2_7_5,
								    3,
								    2_5_9,
								    2_9_7,
								    2_6_0,
								    8_4,
								    4,
								    3_5,
								    1_1_0,
								    4_4,
								    8,
								    2_5_9,
								    9_1,
								    2_6_8,
								    2_1,
								    1_1,
								    2_0_9,
								    2_7_4,
								    1_0_9,
								    2_6_6,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    3_1_5,
								    2_5_8,
								    2_7_8,
								    2_5_8,
								    2_7_7,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_8,
								    2_5_8,
								    3_1_9,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_7,
								    2_5_8,
								    3_1_5,
								    2_5_8,
								    2_8_9,
								    2_5_8,
								    2_7_8,
								    9_9,
								    2_6_9,
								    2_6_6,
								    2_6_2,
								    8,
								    2_5_9,
								    2_4_1,
								    4,
								    2_1_7,
								    2_3_0,
								    2_6_8,
								    2_6_6,
								    5_5,
								    1_6_8,
								    1_0_6,
								    7_5,
								    1_9_3,
								    2_6_6,
								    2_2_3,
								    2_7,
								    4_9,
								    2_6,
								    2_8_2,
								    2_5,
								    2_6_4,
								    2_9_9,
								    1_9,
								    2_6,
								    0,
								    2_5_8,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    1_7_6,
								    1_8_3,
								    2_7_0,
								    1_1,
								    2_6_2,
								    4_2,
								    6_1,
								    2_6_5,
								]

								self.assertListEqual(a	,  self.big_tokenizer.encode(a		)		)







	@require_torch
	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[Any]:
								import torch

								from transformers import ReformerConfig, ReformerModel

								# Build sequence
								__UpperCamelCase      :  Tuple       =   list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
								__UpperCamelCase      :  Union[str, Any]       =   " ".join(a		)
								__UpperCamelCase      :  Tuple       =   self.big_tokenizer.encode_plus(a	,  return_tensors="pt"		)
								__UpperCamelCase      :  Optional[int]       =   self.big_tokenizer.batch_encode_plus([sequence, sequence]	,  return_tensors="pt"		)

								__UpperCamelCase      :  Optional[Any]       =   ReformerConfig()
								# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
								__UpperCamelCase      :  Tuple       =   encoded_sequence["input_ids"].shape
								__UpperCamelCase      :  int       =   ReformerModel(a		)

								# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
															model(**a		)
															model(**a		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Any:
								# fmt: off
								__UpperCamelCase      :  Any       =   {"input_ids": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# This tokenizer does not know some characters like ")".
								# That is the reason why we use very simple texts here.
								# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
								__UpperCamelCase      :  List[str]       =   [
								    "This is a very simple sentence.",
								    "The quick brown fox jumps over the lazy dog.",
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=a	,  model_name="google/reformer-crime-and-punishment"	,  revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"	,  padding=a	,  sequences=a	,  )